The impact of urban form on residential space-conditioning energy use has been controversial in recent planning literature. This study empirically evaluates the association between urban form and residential energy use, focusing particularly on residential electricity use for space cooling in the City of Sacramento, California. We characterize urban form, property conditions, and demographic and socioeconomic characteristics by applying spatial metrics embedded within a geographic information system where LiDAR data effectively include each building and the surrounding vegetation. A statistical model is applied to assess the relationship between these explanatory variables and the estimated summer air-conditioning energy use. Controlling for other variables, higher population density, east—west street orientation, higher green space density, larger vegetation on the east, south, and especially the west sides of houses, appears to have statistically significant effects on reducing summer cooling energy use. This study quantifies the built environment impact on the energy demand of air conditioning and informs planners as they craft urban planning and design policies for energy conservation.

Climate change is making intense DANA (depresión aislada en niveles altos) type rains a more frequent phenomenon in Mediterranean basins. This trend, combined with the transformation of the territory derived from diffuse anthropization processes, has created an explosive cocktail for many coastal towns due to flooding events. To evaluate this problem and the impact of its main guiding parameters, a geostatistical analysis of the territory based on GIS indicators and an NDVI (Normalized Difference Vegetation Index) analysis is developed. The assessment of the validity of a proposed methodology is applied to the case study of the Campo de Cartagena watershed located around the Mar Menor, a Mediterranean coastal lagoon in Southeastern Spain. This area has suffered three catastrophic floods derived from the DANA phenomenon between 2016 and 2019. The results show that apart from the effects derived from climate change, the real issue that amplifies the damage caused by floods is the diffuse anthropization process in the area, which has caused the loss of the natural hydrographic network that traditionally existed in the basin.

Measuring and monitoring the height of vegetation provides important insights into forest age and habitat quality. These are essential for the accuracy of applications that are highly reliant on up-to-date and accurate vegetation data. Current vegetation sensing practices involve ground survey, photogrammetry, synthetic aperture radar (SAR), and airborne light detection and ranging sensors (LiDAR). While these methods provide high resolution and accuracy, their hardware and collection effort prohibits highly recurrent and widespread collection. In response to the limitations of current methods, we designed Y-NET, a novel deep learning model to generate high resolution models of vegetation from highly recurrent multispectral aerial imagery and elevation data. Y-NET’s architecture uses convolutional layers to learn correlations between different input features and vegetation height, generating an accurate vegetation surface model (VSM) at $1\times 1$ m resolution. We evaluated Y-NET on 235 km${}^2$ of the East San Francisco Bay Area and find that Y-NET achieves low error from LiDAR when tested on new locations. Y-NET also achieves an $R^2$ of 0.83 and can effectively model complex vegetation through side-by-side visual comparisons. Furthermore, we show that Y-NET is able to identify instances of vegetation growth and mitigation by comparing aerial imagery and LiDAR collected at different times.

The management and conservation of wetlands and vulnerable protected areas of high ecological value dependent on the existence of water is complex and generally depends on the climate and rainfall in semi-arid territories such as southeastern Spain. However, one variable that is not usually considered sufficiently rigorously in this field of research is the environmental impact of the transformation of the surrounding territory due to anthropic diffuse issues. This phenomenon is not easy to appreciate, since it does not necessarily occur in the environment directly closest to protected areas and it is always difficult to measure and analyze. This study proposes an innovative spatiotemporal methodological framework to evaluate all these phenomena of diffuse anthropization whose indirect impacts on protected areas dependent on the existence of water are currently full of unknowns. Using GIS indicators, a geostatistical analysis based on the concept of the area of influence of diffuse anthropization (AIDA) is proposed to assess the spatial correlation between the anthropic transformation of the territory and the degradation of protected areas over time. The proposal has been applied with a comparative approach to three case studies located in Spain between 2000 and 2020, obtaining clarifying results on the existing spatial correlation patterns between both questions.

The availability of high-resolution remote sensing imagery brings both opportunity and challenge to environmental designers and planners in obtaining high-quality landscape information for better design and planning decision making. To meet the challenge, in this paper we introduce a comprehensive approach to measuring urban vegetation data detailed to single patches of trees or shrubs and single patches of lawn or grass with multisource remote sensing imageries. Methodologically, the approach integrates advanced geospatial technologies to achieve the research objective. First, an automatic registration algorithm is applied to align an unorthorectified QuickBird satellite multispectral imagery to a high-resolution United States Geographical Survey orthoimage. Next, an image segmentation process extracts landscape objects from such multisource data for further object-based image classification. Third, the approach takes advantage of the strong power of a group of prioritized spectral, geometric, topological, and thematic image object features to produce satisfactory classification results. The approach was tested in the Oakland Metropolitan Area in California, USA and was assessed with both groundtruthing and imagetruthing data. The paper concludes with a discussion on the potential applications of both the approach and the generated data in environmental design and planning.

Sea level rise (SLR) and storm surge inundation are major concerns along the coast of the San Francisco Bay (the Bay Area), impacting both coastal communities and critical infrastructure networks. The oil industry comprises a complex and critical infrastructure network located in the Bay Area. There is an urgent need to assess consequences and identify risk-based solutions to increase the resilience of this industrial network in the Bay Area to SLR and storm surge. In this study, a comprehensive multi-modal network model representing the fuel supply system was built. A total of 120 coastal flooding scenarios, including four General Circulation Models, two Representative Concentration Pathways, three percentiles of future SLR estimates, and five planning horizons (20 year intervals from 2000 to 2100) were considered. The impact of coastal flooding on fuel transportation networks was studied at two different scales: regional and local. At the regional scale, basic network properties and network efficiency were analyzed across multiple flooding scenarios. At the local scale, cascading effects of individual node disruptions were simulated. Based on this research, smarter and more holistic risk-based adaptation strategies can be established which could lead to a more resilient fuel transportation network system.